As a method to detect and track targets using radar, sonar and lidar, Multiple Hypothesis Tracking (MHT) is used widely as shown in Japanese Patent Application Laid-Open No. 2009-192550, for example. Because MHT can track a plurality of targets, there are many derived methods which can cope with false detection and false rejection.
Basically, a track of a target is obtained by repeating processing in which a true target position in a current search is estimated from a prediction of a target position for the current search obtained using a detection result of a target position in the last search and a detection result of a target position in the current search, and, from this estimated result, a target position in the next search is predicted.
When performing prediction by MHT, Karmann filter is often used. On the other hand, when a frequency of false detection and false rejection of a target is not high, the scheme of MHT is not used, and targets are often tracked only by Karmann filter.
However, in recent years, a particle filter, as shown in the following document 1, which can also handle a case where a system model and an observation model are of a non-Gaussian system has come to be used widely.    Document 1: Tomoyuki Higuchi, “Particle Filter”, Institute of Electronics, Information and Communication Engineers Journal, Vol. 88, No. 12, 2005.
The more accuracy is improved because a system model and an observation model are followed more correctly, when a particle filter is used, the larger the number of virtual particles is. However, on the other side of a coin, there is a problem that the calculation amount is increased. Further, about the number of virtual particles (hereinafter, also referred to as “virtual particle count”), a guideline for finding the most suitable number of particles has not been proposed until now.